Special Issue "Progresses in Advanced Research on Intelligent Electric Vehicles"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electric Vehicles".

Deadline for manuscript submissions: closed (30 September 2019).

Special Issue Editors

Prof. Dr. Rui Xiong
grade E-Mail Website
Guest Editor
Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, No.5 Zhongguancun Street, Beijing 100081, China
Tel. +86(10)6891-4070; Fax: +86(10)6891-4070
Interests: electrical/hybrid vehicles; electrical/hybrid driven system; energy storage and battery management system
Special Issues and Collections in MDPI journals
Prof. Michael Gerard Pecht
E-Mail Website
Guest Editor
Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
Interests: competitive product development; product characterization and qualification; supply chain creation and management; prognostics and health management; product reliability, risk assessment and mitigation
Special Issues and Collections in MDPI journals
Prof. Caiping Zhang
E-Mail Website
Guest Editor
School of Electrical Engineering, Beijing Jiaotong University, No.3 Shangyuancun, Haidian District, Beijing 100044, China
Interests: battery modeling and states estimation; aging mechanisms and RUL prediction; charging optimizations; battery fault diagnosis and early warning
Prof. Yonggang Liu
E-Mail Website
Guest Editor
State Key Laboratory of Mechanical Transmissions & School of Automotive Engineering, Chongqing University, No.174 Shazhengjie, Shapingba, Chongqing, 400044, China
Interests: optimization and control of intelligent electric vehicle (including EV/HEV) power systems; integrated control of vehicle automatic transmissions
Dr. Yongzhi Zhang
E-Mail Website
Guest Editor
Architecture and civil engineering, Geology and geotechnics, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
Interests: electric vehicles; battery management systems; battery health diagnostics and RUL prognostics

Special Issue Information

Dear Colleagues,

We cordially invite you to contribute to our Special Issue of Energies on the theme of “Progresses in Advanced Research on Intelligent Electric Vehicles”.

In order to build cleaner and more efficient vehicles, automobile industries worldwide have exhibited a clear trend towards developing technology in the direction of fuel decarburization, energy diversification, and power electrification. Electric vehicles (EVs), which represent energy-saving and new energy automobiles, have become an effective way to solve the problems of air pollution, oil shortage, and low efficiency and, thus, help automobile industries to transition into the development of a sustainable method of transport. As such, the research and development of EVs has attracted worldwide attention and taken on an accelerated pace. The International Conference on Electric and Intelligent Vehicles (ICEIV2018), which was held in Australia on Nov 21th–25th, 2018, has provided an excellent forum for scientists, researchers, engineers, and government officials all over the world to present and discuss the latest key EV technologies and development trends.

The ICEIV2018 program featured keynote speeches, workshops, and paper sessions, and the theme of this associated Special Issue is “Progresses in Advanced Research on Intelligent Electric Vehicles”. Topics of interest for this Special Issue include but are not limited to:

  1. Electric/hybrid vehicles;
  2. Intelligent vehicles;
  3. Connected automated vehicles;
  4. Vehicle technology;
  5. Energy storage technology;
  6. Motor and power electronics.

Prof. Rui Xiong
Prof. Michael Gerard Pecht
Prof. Caiping Zhang
Prof. Yonggang Liu
Dr. Yongzhi Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Design optimization
  • Power electronics
  • Electric machines
  • Wireless power transfer
  • Traction drive systems
  • Batteries
  • Battery management
  • Charging systems and infrastructures
  • Electric/hybrid electric vehicles
  • Connected and automated vehicles
  • Smart grid and V2G
  • Transportation electrification
  • Design optimization for vehicle structures
  • Modeling, simulation, and control for electric vehicles

Published Papers (10 papers)

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Research

Open AccessArticle
Stochastic Bilevel Program for Optimal Coordinated Energy Trading of an EV Aggregator
Energies 2019, 12(20), 3813; https://doi.org/10.3390/en12203813 - 09 Oct 2019
Abstract
Gradually replacing fossil-fueled vehicles in the transport sector with Electric Vehicles (EVs) may help ensure a sustainable future. With regard to the charging electric load of EVs, optimal scheduling of EV batteries, controlled by an aggregating agent, may provide flexibility and increase system [...] Read more.
Gradually replacing fossil-fueled vehicles in the transport sector with Electric Vehicles (EVs) may help ensure a sustainable future. With regard to the charging electric load of EVs, optimal scheduling of EV batteries, controlled by an aggregating agent, may provide flexibility and increase system efficiency. This work proposes a stochastic bilevel optimization problem based on the Stackelberg game to create price incentives that generate optimal trading plans for an EV aggregator in day-ahead, intra-day and real-time markets. The upper level represents the profit maximizer EV aggregator who participates in three sequential markets and is called a Stackelberg leader, while the second level represents the EV owner who aims at minimizing the EV charging cost, and who is called a Stackelberg follower. This formulation determines endogenously the profit-maximizing price levels constraint by cost-minimizing EV charging plans. To solve the proposed stochastic bilevel program, the second level is replaced by its optimality conditions. The strong duality theorem is deployed to substitute the complementary slackness condition. The final model is a stochastic convex problem which can be solved efficiently to determine the global optimality. Illustrative results are reported based on a small case with two vehicles. The numerical results rely on applying the proposed methodology to a large scale fleet of 100, 500, 1000 vehicles, which provides insights into the computational tractability of the current formulation. Full article
(This article belongs to the Special Issue Progresses in Advanced Research on Intelligent Electric Vehicles)
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Open AccessArticle
Active Control and Validation of the Electric Vehicle Powertrain System Using the Vehicle Cluster Environment
Energies 2019, 12(19), 3642; https://doi.org/10.3390/en12193642 - 24 Sep 2019
Abstract
With the development of intelligent vehicle technologies, vehicles can obtain more and more information from various sensors. Many researchers have focused on the vertical and horizontal relationships between vehicles in a vehicle cluster environment and control of the vehicle power system. When the [...] Read more.
With the development of intelligent vehicle technologies, vehicles can obtain more and more information from various sensors. Many researchers have focused on the vertical and horizontal relationships between vehicles in a vehicle cluster environment and control of the vehicle power system. When the vehicle is driving in the cluster environment, the powertrain system should quickly respond to the driver’s dynamic demand, so as to achieve the purpose of quickly passing through the cluster environment. The vehicle powertrain system should be regarded as a separate individual to research its active control strategy in a vehicle cluster environment to improve the control effect. In this study, the driving characteristics of vehicles in a cluster environment have been analyzed, and a vehicle power-demanded prediction algorithm based on a vehicle-following model has been proposed in a cluster environment. Based on the vehicle power demand forecast and driver operation, an active control strategy of the vehicle powertrain system has been designed considering the passive control strategy of the powertrain system. The results show that the vehicle powertrain system can ensure a sufficient backup power with the active control proposed in the paper, and the motor efficiency is improved by 0.61% compared with that of the passive control strategy. Moreover, the overall efficiency of the powertrain system is increased by 0.6% and the effectiveness of the active control is validated using the vehicle cluster environment. Full article
(This article belongs to the Special Issue Progresses in Advanced Research on Intelligent Electric Vehicles)
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Open AccessArticle
Nonlinear Synchronous Control for H-Type Gantry Stage Used in Electric Vehicles Manufacturing
Energies 2019, 12(12), 2305; https://doi.org/10.3390/en12122305 - 17 Jun 2019
Abstract
The H-type gantry stage (HGS) is widely used in electric vehicle manufacturing and other fields. However, resulting from the existence of mechanical coupling, the synchronous control problem of HGS always troubles many engineers. Most synchronization schemes were either engaged in improving each motor’s [...] Read more.
The H-type gantry stage (HGS) is widely used in electric vehicle manufacturing and other fields. However, resulting from the existence of mechanical coupling, the synchronous control problem of HGS always troubles many engineers. Most synchronization schemes were either engaged in improving each motor’s tracking performance or committed to pure motion synchronization only. However, tracking and synchronous performance are interconnected, because of the mechanical coupling. In this paper, a rigid assumed system model of HGS, concerning the effects of mid-beam rotary inertia, mid-beam stiffness, and end-effector movement, is presented. Based on the proposed model, an adaptive robust synchronous control based on a rigid assumed model (ARSCR) is proposed to improve both synchronous and tracking performance of the HGS. From the Lyapunov analysis, the proposed ARSCR can achieve the convergence of synchronous error and tracking error, simultaneously. An HGS driven by dual linear motors is built and used to perform the experimental verification. The experimental results indicate the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Progresses in Advanced Research on Intelligent Electric Vehicles)
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Open AccessArticle
Control Strategy for Active Hierarchical Equalization Circuits of Series Battery Packs
Energies 2019, 12(11), 2071; https://doi.org/10.3390/en12112071 - 30 May 2019
Abstract
Most series battery active equalization circuits implement the equalization first within the series and then between the series, which restricts the equilibrium speed. A hierarchical equalization circuit topology based on the Buck-Boost module is applied in this paper. The equalization is divided into [...] Read more.
Most series battery active equalization circuits implement the equalization first within the series and then between the series, which restricts the equilibrium speed. A hierarchical equalization circuit topology based on the Buck-Boost module is applied in this paper. The equalization is divided into two different equalization processes according to the equilibrium energy flow. The two equalization processes can be performed simultaneously, and the currents in the different hierarchical circuits do not affect each other, thus achieving simultaneous equalizations within the series and between the series. An equalization condition of the terminal voltage is applied and simulations and experiments on charge, discharge, and static equalizations in the four series-connected ternary lithium-ion batteries are performed. Full article
(This article belongs to the Special Issue Progresses in Advanced Research on Intelligent Electric Vehicles)
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Open AccessArticle
Multi-Objective Hybrid Optimization Algorithm Using a Comprehensive Learning Strategy for Automatic Train Operation
Energies 2019, 12(10), 1882; https://doi.org/10.3390/en12101882 - 17 May 2019
Abstract
Aiming at the problem of easy-to-fall-into local convergence for automatic train operation (ATO) velocity ideal trajectory profile optimization algorithms, an improved multi-objective hybrid optimization algorithm using a comprehensive learning strategy (ICLHOA) is proposed. Firstly, an improved particle swarm optimization algorithm which adopts multiple [...] Read more.
Aiming at the problem of easy-to-fall-into local convergence for automatic train operation (ATO) velocity ideal trajectory profile optimization algorithms, an improved multi-objective hybrid optimization algorithm using a comprehensive learning strategy (ICLHOA) is proposed. Firstly, an improved particle swarm optimization algorithm which adopts multiple particle optimization models is proposed, to avoid the destruction of population diversity caused by single optimization model. Secondly, to avoid the problem of random and blind searching in iterative computation process, the chaotic mapping and the reverse learning mechanism are introduced into the improved whale optimization algorithm. Thirdly, the improved archive mechanism is used to store the non-dominated solutions in the optimization process, and fusion distance is used to maintain the diversity of elite set. Fourthly, a dual-population evolutionary mechanism using archive as an information communication medium is designed to enhance the global convergence improvement of hybrid optimization algorithms. Finally, the optimization results on the benchmark functions show that the ICLHOA can significantly outperform other algorithms for contrast. Furthermore, the ATO Matlab/simulation and hardware-in-the-loop simulation (HILS) results show that the ICLHOA has a better optimization effect than that of the traditional optimization algorithms and improved algorithms. Full article
(This article belongs to the Special Issue Progresses in Advanced Research on Intelligent Electric Vehicles)
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Open AccessArticle
Optimal Coordinated Bidding of a Profit Maximizing, Risk-Averse EV Aggregator in Three-Settlement Markets Under Uncertainty
Energies 2019, 12(9), 1755; https://doi.org/10.3390/en12091755 - 09 May 2019
Cited by 2
Abstract
This paper develops a two-stage stochastic and dynamically updated multi-period mixed integer linear program (SD-MILP) for optimal coordinated bidding of an electric vehicle (EV) aggregator to maximize its profit from participating in competitive day-ahead, intra-day and real-time markets. The hourly conditional value at [...] Read more.
This paper develops a two-stage stochastic and dynamically updated multi-period mixed integer linear program (SD-MILP) for optimal coordinated bidding of an electric vehicle (EV) aggregator to maximize its profit from participating in competitive day-ahead, intra-day and real-time markets. The hourly conditional value at risk (T-CVaR) is applied to model the risk of trading in different markets. The objective of two-stage SD-MILP is modeled as a convex combination of the expected profit and the T-CVaR hourly risk measure. When day-ahead, intra-day and real-time market prices and fleet mobility are uncertain, the proposed two-stage SD-MILP model yields optimal EV charging/discharging plans for day-ahead, intra-day and real-time markets at per device level. The degradation costs of EV batteries are precisely modeled. To reflect the continuous clearing nature of the intra-day and real-time markets, rolling planning is applied, which allows re-forecasting and re-dispatching. The proposed two-stage SD-MILP is used to derive a bidding curve of an aggregator managing 1000 EVs. Furthermore, the model statistics and computation time are recorded while simulating the developed algorithm with 5000 EVs. Full article
(This article belongs to the Special Issue Progresses in Advanced Research on Intelligent Electric Vehicles)
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Open AccessArticle
Hierarchical Distributed Control Strategy for Electric Vehicle Mobile Energy Storage Clusters
Energies 2019, 12(7), 1195; https://doi.org/10.3390/en12071195 - 27 Mar 2019
Abstract
The stability problem of the power system becomes increasingly important for the penetration of renewable energy resources (RESs). The inclusion of electric vehicles (EVs) in a power system can not only promote the consumption of RESs, but also provide energy for the power [...] Read more.
The stability problem of the power system becomes increasingly important for the penetration of renewable energy resources (RESs). The inclusion of electric vehicles (EVs) in a power system can not only promote the consumption of RESs, but also provide energy for the power grid if necessary. As a mobile energy storage unit (MESU), EVs should pay more attention to the service life of their batteries during operation. A hierarchical distributed control strategy was proposed in this paper for mobile energy storage clusters (MESCs) considering the life loss of each EV’s battery. This strategy was divided into a two-layer control structure. Firstly, numerous EVs were divided into different clusters according to their regional relationships. The lower layer adopted a distributed collaborative control approach for allocating energy among EVs in the cluster. Under this condition, an aggregate EVs response model was established and the characteristic of the MESC was analyzed. Secondly, the upper layer applied the multi-agent consensus algorithm to achieve the optimal allocation among different clusters. Therefore, the control strategy realized the two-way communication of energy between EVs and the power grid, and ensured the optimal economical dispatch for the mobile energy storage system (MESS). Finally, the simulation of testing examples verified the effectiveness of the proposed strategy. Full article
(This article belongs to the Special Issue Progresses in Advanced Research on Intelligent Electric Vehicles)
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Open AccessArticle
Torque Optimal Allocation Strategy of All-Wheel Drive Electric Vehicle Based on Difference of Efficiency Characteristics between Axis Motors
Energies 2019, 12(6), 1122; https://doi.org/10.3390/en12061122 - 22 Mar 2019
Abstract
All-wheel drive is an important technical direction for the future development of pure electric vehicles. The difference in the efficiency distribution of the shaft motor caused by the optimal load matching and motor manufacturing process, the traditional torque average distribution strategy is not [...] Read more.
All-wheel drive is an important technical direction for the future development of pure electric vehicles. The difference in the efficiency distribution of the shaft motor caused by the optimal load matching and motor manufacturing process, the traditional torque average distribution strategy is not applicable to the torque distribution of the all-wheel drive power system. Aiming at the above problems, this paper takes the energy efficiency of power system as the optimization goal, proposes a dynamic allocation method to realize the torque distribution of electric vehicle all-wheel drive power system, and analyzes and verifies the adaptability of this optimization algorithm in different urban passenger vehicle working cycles. The simulation results show that, compared with the torque average distribution method, the proposed method can effectively solve the problem that the difference of the efficiency distribution of the two shaft motors in the power system affects the energy consumption of the power system. The energy consumption rate of the proposed method is reduced by 5.96% and 5.69%, respectively, compared with the average distribution method under the China urban passenger driving cycle and the Harbin urban passenger driving cycle. Full article
(This article belongs to the Special Issue Progresses in Advanced Research on Intelligent Electric Vehicles)
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Open AccessArticle
Accurate and Efficient Estimation of Lithium-Ion Battery State of Charge with Alternate Adaptive Extended Kalman Filter and Ampere-Hour Counting Methods
Energies 2019, 12(4), 757; https://doi.org/10.3390/en12040757 - 25 Feb 2019
Cited by 2
Abstract
State of charge (SOC) estimation is a key issue in battery management systems. The challenge lies in balancing the trade-off between accuracy and computation cost. To this end, we propose an alternate method by combining the ampere-hour integral (AHI) method which has low [...] Read more.
State of charge (SOC) estimation is a key issue in battery management systems. The challenge lies in balancing the trade-off between accuracy and computation cost. To this end, we propose an alternate method by combining the ampere-hour integral (AHI) method which has low computation cost, and the adaptive extended Kalman filter (AEKF) method, which has high accuracy. The technical viability of this alternate method is verified on a LiMnO2-LiNiO2 battery module with a nominal capacity of 130 Ah under the New European Driving Cycle (NEDC) condition. Drifts in current and voltage measurement are considered. The experimental results show that the absolute SOC error using the AHI method monotonously increases from 0% to 7.2% with the computation time of 10 s while the calculation time is obtained on a ThinkPad E450 PC with an Intel Core i7-5500U CPU @2.40 GHz and 16.0 GB RAM. The absolute SOC error of the AEKF method maintains within 3.5% with the computation time of 49 s. Therefore, the alternate method almost maintains the same SOC accuracy compared to the AEKF method which reduces the maximum absolute SOC error by 50% compared to the AHI method. Therefore, the alternate method almost has the same computation time compared with the AHI method which reduces the computation time by nearly 75% compared to the AEKF method. Full article
(This article belongs to the Special Issue Progresses in Advanced Research on Intelligent Electric Vehicles)
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Open AccessArticle
Comparison Study of Induction Motor Models Considering Iron Loss for Electric Drives
Energies 2019, 12(3), 503; https://doi.org/10.3390/en12030503 - 05 Feb 2019
Abstract
In a variety of motor models, the effects of iron-loss (ILS) on motor control accuracy and efficiency are generally ignored. This makes it difficult for the motor control system to obtain accurate control parameters (especially on high speed and low load conditions), and [...] Read more.
In a variety of motor models, the effects of iron-loss (ILS) on motor control accuracy and efficiency are generally ignored. This makes it difficult for the motor control system to obtain accurate control parameters (especially on high speed and low load conditions), and limits the improvement of motor control accuracy. This paper aims to clarify the influence of different ILS modeling and observation methods on motor control performance. Three equivalent models of motors with iron losses are compared. These models are: A parallel model, a series model and the simplified traditional model. Three tests are conducted to obtain the effect of ILS perturbation on ILS estimation results, and then to derive the sensitivity of the motor state and torque to the perturbation. These test conditions include: Ideal no-load, heavy-load, locked-rotor, and ILS perturbations during speed regulation. Simulation results show that the impedance and excitation characteristics of the series model and the parallel model are similar, and the traditional model has the best speed regulation smoothness. The ILS estimation errors of the series model is nearly constant and easy to compensate. For accurate ILS observation results, the series model can achieve better control accuracy. Full article
(This article belongs to the Special Issue Progresses in Advanced Research on Intelligent Electric Vehicles)
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